JupyterCon 2018, NYC August 21–25 – Jupyter Blog

… concerns, new laws (GDPR), the evolution of computation, plus good storytelling and communication in general — as we’ll explore with practitioners throughout the conference.

Recent beta release of JupyterLab embodies the meta-theme of extensible software architecture for interactive computing with data. While many people think of Jupyter as a “notebook,” that’s merely one building block needed for interactive computing with data. Other building blocks include terminals, file browsers, LaTeX, markdown, rich outputs, text editors, and renderers/viewers for different data formats. JupyterLab is the next…
2018-03-15 18:30:47.325000+00:00 https://blog.jupyter.org/jupytercon-2018-nyc-august-21-25-5571d7454d5b?source=collection_home—6——2—————-

CloudQuant Thoughts: CloudQuant has been using the beta version of JupyterLab for our internal portfolio managers in their research for Alpha Signals. This platform is very useful in for the data science portion of algorithm development.

Reproducible Data Dependencies for Python [Guest Post]

… open source projects in the Jupyter ecosystem and the problems they attempt to solve. If you would like to submit a guest post to highlight a specific tool or project, please get in touch with us.

Does Deep Learning Represent A New Paradigm In Software Development?

… to tune the model.

Weidman gives an image classification example, wherein to train an image classifier, the developer loads in the images, and then uses an off-the-shelf code from a library like Keras to show the model structure. By searching how to perform “image classifier keras”, a developer can train a model with less than 20 lines of code.

Given this scenario, Weidman argues for budding data scientists entering the field, knowing how a model works would not be mission critical. The more important thing would be ensuring data quality and building the right checks for the model, he adds.

Google Launches Machine Learning Course for the World

…a researcher, an entrepreneur, a professional, the course is for anyone and everyone.

The Machine Learning course is a crash course provided by Google, which provides hands-on practice on TensorFlow APIs along with video lectures and various lessons. TensorFlow is a Machine Learning library provided by Google, which focuses on building machine learning products and tools. The course provided by Google consists of the following:

What Is TensorLayer & How Does It Differ From TensorFlow ML Libraries?

…sourcing most of their work.

We explore one such open-source DL and RL software library called TensorLayer, which is a part of Google’s popular machine learning and numerical computational framework TensorFlow. The idea behind the new library was to facilitate a modular approach to DL as well as RL to tackle complexity and iterative tasks when it comes to large neural networks and their interactions. It was first released in 2016 and gradually adopted changes along the way to become the most sought after libraries for DL.The entire code for TensorLayer is written in Python – the most preferred programm…
2018-04-05 12:08:52+00:00 https://analyticsindiamag.com/what-is-tensorlayer-and-how-is-it-different-from-tensorflows-other-machine-learning-libraries/

Google Launches Machine Learning Course for the World

…ent, a researcher, an entrepreneur, a professional, the course is for anyone and everyone.

The Machine Learning course is a crash course provided by Google, which provides hands-on practice on TensorFlow APIs along with video lectures and various lessons. TensorFlow is a Machine Learning library provided by Google, which focuses on building machine learning products and tools. The course provided by Google consists of the following:

How Alibaba Used Reinforcement Learning To Change Real-Time Bidding

…ns, it is very hard to find the accurate data. The upperhand of the use of simulator is the auctions with unique bids which can be simulated with the help of the entire auction database.

Distributed TensorFlow Cluster:

The RL model is trained on the tensorflow cluster in a varied manner with the servers to facilitate the handling of the weights in the layers. The model was ran on a number of CPUs and GPUs to parallely input the billions of samples since agents have to be trained simultaneously.

KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. To evaluate any technique we generally look at 3 important aspects:

Sponsored Content: Training Machine Learning Models with MongoDB

…duplicate URLs, and their associated text data, were not added to the database.

Next, the entire dataset needed to be parsed using NLP and passed in as training data for the TFIDF Vectorizer (in the scikit-learn toolkit) and the Latent Dirichlet Allocation (LDA) model. Since both TFIDF and LDA require training on the entire dataset (represented by a matrix of ~70k rows x ~250k columns), I needed to store a lot of information in memory. LDA requires training on non-reduced data in order to identify correlations between all features in their original space. Scikit Learn’s implementations of TFIDF and LDA a…
2018-04-02 00:00:00 http://www.dbta.com/Editorial/Actions/Sponsored-Content-Training-Machine-Learning-Models-with-MongoDB-123586.aspx

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